Active Feature Acquisition with Generative Surrogate Models
Authors: Yang Li, Junier Oliva
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our method on several benchmark environments built upon the UCI repository (Dua & Graff, 2017) and MNIST dataset (Le Cun, 1998). We compare our method to another RL based approach, JAFA (Shim et al., 2018), which jointly trains an agent and a classifier. We also compare to a greedy policy EDDI (Ma et al., 2018) that estimates the utility for each candidate feature using a VAE based model and selects one feature with the highest utility at each acquisition step. As a baseline, we also acquire features greedily using our surrogate model that estimates the utility following (6), (8) and (9). We use a fixed cost for each feature and report multiple results with different α in (1) to control the trade-off between task performance and acquisition cost. We cross validate the best architecture and hyperparameters for baselines. Architectural details, hyperparameters and sensitivity analysis are pro- Active Feature Acquisition with Generative Surrogate Models vided in the appendix. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of North Carolina at Chaple Hill, Chapel Hill, NC, USA. Correspondence to: Yang Li <yangli95@cs.unc.edu>, Junier B. Oliva <joliva@cs.unc.edu>. |
| Pseudocode | Yes | Please refer to Algorithm 1 for pseudo-code of the acquisition process with our GSMRL framework. Please also see Algorithm 2 in the appendix for a detailed version. |
| Open Source Code | Yes | We open-source a standardized environment inheriting the Open AI gym interfaces (Brockman et al., 2016) to assist future research on active feature acquisition. Code is publicly available at https:// github.com/lupalab/GSMRL. |
| Open Datasets | Yes | In this section, we evaluate our method on several benchmark environments built upon the UCI repository (Dua & Graff, 2017) and MNIST dataset (Le Cun, 1998). |
| Dataset Splits | Yes | We cross validate the best architecture and hyperparameters for baselines. Architectural details, hyperparameters and sensitivity analysis are pro- Active Feature Acquisition with Generative Surrogate Models vided in the appendix. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | Our implementation is based on PyTorch (Paszke et al., 2019) and Python 3.8. |
| Experiment Setup | No | Architectural details, hyperparameters and sensitivity analysis are pro- Active Feature Acquisition with Generative Surrogate Models vided in the appendix. |